Member of Technical Staff

About the Role

Join a small team at an early-stage AI infrastructure startup, building and operating systems that make large language model inference faster and more cost-efficient. The role combines low-level performance engineering, infrastructure operations, and customer problem-solving.

What You'll Do

• Bring new open-source models into production with a focus on latency and throughput.

• Optimize inference systems, including batching, KV cache, speculative decoding, and quantization.

• Write and tune kernels using CUDA, HIP, and Triton across a range of accelerator platforms.

• Design, deploy, and operate heterogeneous clusters across infrastructure vendors.

• Improve the reliability, observability, and cost efficiency of production inference fleets.

• Work with customers to understand performance needs and help set technical priorities.

What We're Looking For

• Typically 1 to 6 years of relevant experience, with strong new graduates also considered.

• Strong computer science fundamentals, including understanding of computer architecture, memory hierarchy, and operating system internals.

• Experience in backend, infrastructure, or systems engineering, rather than exclusively full-stack or web development.

• Familiarity with CUDA, Triton, or GPU programming, and ideally experience with inference optimization or large-scale cluster operations.

• Coursework or practical experience in operating systems, computer architecture, or compilers.

• Evidence of exceptional technical work, strong communication skills, and experience using AI coding tools to improve engineering workflows.

Compensation & Benefits

Salary range: $200,000 to $300,000 annually. Visa sponsorship is available.

Location

Remote within the United States. San Francisco, California is the primary location.